Impact of Shortened Winter Road Access on Costs of Forest Operations
Why this work is in the frame
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Bibliographic record
Abstract
A significant portion of the forest harvesting in the cooler regions of North America occurs in the winter when the ground is frozen and can support machine traffic. Climate change may influence the cost of forestry operations by reducing the period of winter access in those cold regions. In this study, we examined the impact of a shortened period of frozen ground conditions on logging operation and costs. To adapt to shorter period of frozen soil conditions, logging contractors might need to provide more machines and labor to complete logging in a shorter period of frozen conditions. The objectives were to calculate the costs of logging operations of a hypothetical forestry company in Alberta, Canada under two conditions: first, when the wood was hauled to the mill directly; and second, when part of the wood was hauled to satellite yards close to the logging area, thereby minimizing the annual number of idle hauling trucks. General Circulation Models were used to predict future winter weather conditions. Using the current type of harvesting machines and hauling directly to the mill, the unit cost of logging operations ($/m3) was projected to increase by an average of 1.6% to 2.5% in 2030s, 2.8% to 5.3% in the 2050s and 4.8% to 10.9% in the 2080s compared to the base year of 2015–2016. With use of satellite yards during the winter logging, the total logging cost will increase over direct haul, by 1.8% to 2.8% in the 2030s, 3.1% to 5.7% in the 2050s and 5.2% to 11.4% in the 2080s. Using satellite yards, however, will provide year-around employment for hauling truckers and more consistent and reliable hauling operations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it